Preparations

Before assessing data with ‘CellScore’, the users should first create the abbreviations of the cell types to be tested by ‘CellScore’ and a ‘cell.change’ data frame.

Creating a map between cell types and their abbreviations

Users of CellScore should define abbreviations for the cell types they want to test. Each abbreviation should be unique and has a 1-to-1 mapping to a cell type.

Function ‘getCellTypeMap’ in this example returns a map between cell types and their abbreviations that are used in this tutorial. Note that this function is not part of the ‘CellScore’ library and it is only for demonstration purpose in this tutorial.

getCellTypeMap <- function() {
    ## Hardcode abbreviations for each cell type
    cellTypes <- c(
        "RPE",
        "ESC",
        "ESC.d",
        "ESC.t",
        "iPS",  ## check the comment
        "IPS",
        "URI",
        "PBMC",
        "HUVEC",

        "derived cardiomyocyte",
        "hepatocyte-like cell",
        "hepatocyte",
        "fetal liver",
        "derived dopaminergic neuron",
        "derived neuron",
        "spinal cord",
        "derived neural progenitor cell",
        "derived RPE",
        "fetal RPE",
        "fetal heart",
        "derived neural stem cell",
        "naive IPS",
        "naive ESC",
        "fetal brain",
        "derived pancreatic b-like cell",
        "derived endoderm",
        "derived foregut endoderm",
        "derived hepatic endoderm",
        "derived early hepatocyte",
        "derived late hepatocyte",
        "fibroblast",
        "induced neuron",
        "fibroblast.treated",
        "cardiomyocytes",
        "hepatocytes",
        "dopaminergic neurons",
        "neurons",
        "neual progenitor cell",
        "neural stem cells",
        "pancreatic beta cell",
        "endoderm",
        "foregut endoderm",
        "hepatic endoderm",
        "neuron",
        "lymphocyte")

    abbrv <- c(
        ## Original abbreviations
        "RPE",
        "ESC",
        "ESC.d",
        "ESC.t",
        "IPS",
        "IPS",
        "URI",
        "PBMC",
        "HUVEC",
        ## Abbreviations named by me
        "dCDM",
        "HPTL",
        "HPT",
        "fLIV",
        "dDPNEU",
        "dNEU",
        "SPC",
        "dNEUPR",
        "dRPE",
        "fRPE",
        "fHRT",
        "dNEUST",
        "nIPS",
        "nESC",
        "fBRN",
        "dPCB",
        "dEDD",
        "dFEDD",
        "dHPTEDD",
        "dEHPT",
        "dLHPT",
        "FIB",
        "INEU",
        "FIB.t",
        "CDM",
        "HPT",
        "DPNEU",
        "NEU",
        "NEUPR",
        "NEUST",
        "PCB",
        "EDD",
        "FEDD",
        "HPTEDD",
        "NEU",
        "LPC")

    cellTypeMap <- setNames(abbrv, cellTypes)
    
    # Mapping from full cell type names to their abbrvs
    print(cellTypeMap)
    cellTypeMap
}

Prepare ‘cell.change’ data frame

The ‘prepareSampleTransitions’ prepares ‘cell.change’ data frame. This function is not part of ‘CellScore’ library. function ‘prepareSampleTransitions’ is used to generate and save the ‘cell.change’ in a tsv file and is not part of ‘CellScore’ library and only used in this tutorial.

‘cell.change’ data frame contains 3 columns, which are ‘start’, ‘test’ and ‘target’. Each row in the data frame represents the transition in an experiment. The values in the ‘start’ column are the abbreviations of parental cell types (or starting cell types) in the transitions. The values in ‘target’ column are the abbreviations of the target cell types in the transitions. The values in ‘test’ column are in the format ‘{test_cell_type}-{start}’, where ‘{test_cell_type}’ are the abbreviations of the name assigned to the test cells and ‘{start}’ are the abbreviations of parental cell types (or starting cell types). The users should create ‘cell.change’ data frame based on their own experiments

sampleMetadataPath <- system.file("extdata", 
                                  "sampleMetadata.csv", 
                                  package = "CellScore")

transitionPath <- system.file("extdata", 
                              "exampleTransitionsHsapienData.tsv",
                              package="CellScore")

prepareSampleTransitions <- function(sampleMetadataPath, transitionPath) {

    dat <- read.csv(sampleMetadataPath, header = TRUE)

    uniqueCellTypes <- unique(c(as.array(dat$general_cell_type),
                                as.array(dat$target_cell_type),
                                as.array(dat$parental_cell_type)))
    
    # Show all the unique cell types in the data
    print(uniqueCellTypes)

    cellTypeMap <- getCellTypeMap()

    uniqueTransitions <- unique(dat[dat$category == 'test',
                                    c('parental_cell_type',
                                      'general_cell_type',
                                      'target_cell_type')])

    colnames(uniqueTransitions) <- c("start",   "test", "target")

    ## Exclude samples without parental cell types or without target cell type
    uniqueTransitions <- uniqueTransitions[!is.na(uniqueTransitions$start) &
                                             !is.na(uniqueTransitions$target) &
                                             uniqueTransitions$start != '' &
                                             uniqueTransitions$target != '', ]

    ## Replace full cell type names with abbreviations
    uniqueTransitionsAbbrv <- as.data.frame(lapply(uniqueTransitions,
                                                   function(element) {
                                                      (cellTypeMap[element])
                                                   }))

    
    ## Format 'test' column
    uniqueTransitionsAbbrv$test <- paste(uniqueTransitionsAbbrv$test,
                                         uniqueTransitionsAbbrv$start,
                                         sep = '-')

    tempDirPath <- paste(getwd(), 'temp', sep = '/')
    dir.create(tempDirPath)

    write.table(uniqueTransitionsAbbrv,
                file = transitionPath,
                quote = FALSE,
                sep = '\t')

    uniqueTransitionsAbbrv
}

Calculate ‘CellScore’

Generate ‘OnOff’ score by run ‘OnOff’ function with ‘cell.change’ data frame and experiment data.

First, load the dependencies and

# Load dependencies
library(SummarizedExperiment)
## Loading required package: GenomicRanges
## Loading required package: stats4
## Loading required package: BiocGenerics
## Loading required package: parallel
## 
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:parallel':
## 
##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
##     clusterExport, clusterMap, parApply, parCapply, parLapply,
##     parLapplyLB, parRapply, parSapply, parSapplyLB
## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
## 
##     anyDuplicated, append, as.data.frame, basename, cbind, colnames,
##     dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
##     grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
##     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
##     rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
##     union, unique, unsplit, which, which.max, which.min
## Loading required package: S4Vectors
## 
## Attaching package: 'S4Vectors'
## The following object is masked from 'package:base':
## 
##     expand.grid
## Loading required package: IRanges
## Loading required package: GenomeInfoDb
## Loading required package: Biobase
## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.
## Loading required package: DelayedArray
## Loading required package: matrixStats
## 
## Attaching package: 'matrixStats'
## The following objects are masked from 'package:Biobase':
## 
##     anyMissing, rowMedians
## 
## Attaching package: 'DelayedArray'
## The following objects are masked from 'package:matrixStats':
## 
##     colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
## The following objects are masked from 'package:base':
## 
##     aperm, apply, rowsum
library(CellScore)
library(DEE2HsapienData)

# create 'cell.change' data frame and get the map 'CellTypeMap' between cell 
# types and their abbreviations
prepareSampleTransitions(sampleMetadataPath, transitionPath)
##  [1] "RPE"                            "ESC"                           
##  [3] "ESC.d"                          "derived cardiomyocyte"         
##  [5] "ESC.t"                          "hepatocyte-like cell"          
##  [7] "hepatocyte"                     "fetal liver"                   
##  [9] "derived dopaminergic neuron"    "derived neuron"                
## [11] "spinal cord"                    "derived neural progenitor cell"
## [13] "derived RPE"                    "fetal RPE"                     
## [15] "iPS"                            "fetal heart"                   
## [17] "derived neural stem cell"       "IPS"                           
## [19] "naive IPS"                      "naive ESC"                     
## [21] "fetal brain"                    "derived pancreatic b-like cell"
## [23] "derived endoderm"               "derived foregut endoderm"      
## [25] "derived hepatic endoderm"       "derived early hepatocyte"      
## [27] "derived late hepatocyte"        "fibroblast"                    
## [29] "induced neuron"                 "fibroblast.treated"            
## [31] NA                               "cardiomyocytes"                
## [33] "hepatocytes"                    "dopaminergic neurons"          
## [35] "neurons"                        "neual progenitor cell"         
## [37] "neural stem cells"              "pancreatic beta cell"          
## [39] "endoderm"                       "foregut endoderm"              
## [41] "hepatic endoderm"               "neuron"                        
## [43] ""                               "lymphocyte"                    
## [45] "URI"                            "PBMC"                          
## [47] "HUVEC"                         
##                            RPE                            ESC 
##                          "RPE"                          "ESC" 
##                          ESC.d                          ESC.t 
##                        "ESC.d"                        "ESC.t" 
##                            iPS                            IPS 
##                          "IPS"                          "IPS" 
##                            URI                           PBMC 
##                          "URI"                         "PBMC" 
##                          HUVEC          derived cardiomyocyte 
##                        "HUVEC"                         "dCDM" 
##           hepatocyte-like cell                     hepatocyte 
##                         "HPTL"                          "HPT" 
##                    fetal liver    derived dopaminergic neuron 
##                         "fLIV"                       "dDPNEU" 
##                 derived neuron                    spinal cord 
##                         "dNEU"                          "SPC" 
## derived neural progenitor cell                    derived RPE 
##                       "dNEUPR"                         "dRPE" 
##                      fetal RPE                    fetal heart 
##                         "fRPE"                         "fHRT" 
##       derived neural stem cell                      naive IPS 
##                       "dNEUST"                         "nIPS" 
##                      naive ESC                    fetal brain 
##                         "nESC"                         "fBRN" 
## derived pancreatic b-like cell               derived endoderm 
##                         "dPCB"                         "dEDD" 
##       derived foregut endoderm       derived hepatic endoderm 
##                        "dFEDD"                      "dHPTEDD" 
##       derived early hepatocyte        derived late hepatocyte 
##                        "dEHPT"                        "dLHPT" 
##                     fibroblast                 induced neuron 
##                          "FIB"                         "INEU" 
##             fibroblast.treated                 cardiomyocytes 
##                        "FIB.t"                          "CDM" 
##                    hepatocytes           dopaminergic neurons 
##                          "HPT"                        "DPNEU" 
##                        neurons          neual progenitor cell 
##                          "NEU"                        "NEUPR" 
##              neural stem cells           pancreatic beta cell 
##                        "NEUST"                          "PCB" 
##                       endoderm               foregut endoderm 
##                          "EDD"                         "FEDD" 
##               hepatic endoderm                         neuron 
##                       "HPTEDD"                          "NEU" 
##                     lymphocyte 
##                          "LPC"
## Warning in dir.create(tempDirPath): '/home/siyuan/CellScore/inst/temp' already
## exists
##    start        test target
## 1    ESC    dCDM-ESC    CDM
## 2    ESC    HPTL-ESC    HPT
## 3    ESC  dDPNEU-ESC  DPNEU
## 4    IPS    dNEU-IPS    NEU
## 5    ESC    dNEU-ESC    NEU
## 6    ESC  dNEUPR-ESC  NEUPR
## 7    ESC    dRPE-ESC    RPE
## 8    IPS    dRPE-IPS    RPE
## 9    IPS    dCDM-IPS    CDM
## 10   IPS  dNEUST-IPS  NEUST
## 11   IPS    dPCB-IPS    PCB
## 12   ESC    dEDD-ESC    EDD
## 13   ESC   dFEDD-ESC   FEDD
## 14   ESC dHPTEDD-ESC HPTEDD
## 15   ESC   dEHPT-ESC    HPT
## 16   ESC   dLHPT-ESC    HPT
## 17   IPS    dEDD-IPS    EDD
## 18   IPS   dFEDD-IPS   FEDD
## 19   IPS dHPTEDD-IPS HPTEDD
## 20   IPS   dEHPT-IPS    HPT
## 21   IPS   dLHPT-IPS    HPT
## 22   FIB    INEU-FIB    NEU
cell.change <- read.delim(
    file=transitionPath, 
    sep="\t",
    header=TRUE, 
    stringsAsFactors=FALSE)
CellTypeMap <- getCellTypeMap()
##                            RPE                            ESC 
##                          "RPE"                          "ESC" 
##                          ESC.d                          ESC.t 
##                        "ESC.d"                        "ESC.t" 
##                            iPS                            IPS 
##                          "IPS"                          "IPS" 
##                            URI                           PBMC 
##                          "URI"                         "PBMC" 
##                          HUVEC          derived cardiomyocyte 
##                        "HUVEC"                         "dCDM" 
##           hepatocyte-like cell                     hepatocyte 
##                         "HPTL"                          "HPT" 
##                    fetal liver    derived dopaminergic neuron 
##                         "fLIV"                       "dDPNEU" 
##                 derived neuron                    spinal cord 
##                         "dNEU"                          "SPC" 
## derived neural progenitor cell                    derived RPE 
##                       "dNEUPR"                         "dRPE" 
##                      fetal RPE                    fetal heart 
##                         "fRPE"                         "fHRT" 
##       derived neural stem cell                      naive IPS 
##                       "dNEUST"                         "nIPS" 
##                      naive ESC                    fetal brain 
##                         "nESC"                         "fBRN" 
## derived pancreatic b-like cell               derived endoderm 
##                         "dPCB"                         "dEDD" 
##       derived foregut endoderm       derived hepatic endoderm 
##                        "dFEDD"                      "dHPTEDD" 
##       derived early hepatocyte        derived late hepatocyte 
##                        "dEHPT"                        "dLHPT" 
##                     fibroblast                 induced neuron 
##                          "FIB"                         "INEU" 
##             fibroblast.treated                 cardiomyocytes 
##                        "FIB.t"                          "CDM" 
##                    hepatocytes           dopaminergic neurons 
##                          "HPT"                        "DPNEU" 
##                        neurons          neual progenitor cell 
##                          "NEU"                        "NEUPR" 
##              neural stem cells           pancreatic beta cell 
##                        "NEUST"                          "PCB" 
##                       endoderm               foregut endoderm 
##                          "EDD"                         "FEDD" 
##               hepatic endoderm                         neuron 
##                       "HPTEDD"                          "NEU" 
##                     lymphocyte 
##                          "LPC"
# Path to the data to be assessed 
rdata.path <- system.file("extdata", "deseq2NormalizedDee2Data",
  package="CellScore")
 
# Load preprocessed hsapien data in SummarizedExperiment format from DEE2
deseq2NormalizedDee2Data <- readRDS(rdata.path)
normalizedSExpr <- deseq2NormalizedDee2Data$normalizedSExpr


colData(normalizedSExpr)$SRR_accession <- rownames(colData(normalizedSExpr))

# replace values in 'general_cell_type' with abbrvs 
colData(normalizedSExpr)[, 'general_cell_type'] <- unname(as.array(sapply(
  normalizedSExpr$general_cell_type, 
  function(element) CellTypeMap[[element]], USE.NAMES = FALSE)))

# replace values in 'parental_cell_type' with abbrvs
colData(normalizedSExpr)[, 'parental_cell_type'] <- unname(as.array(sapply(
  normalizedSExpr$parental_cell_type, 
  function(element) {
    if (is.na(element)) {
      return(NA)
    }
    CellTypeMap[[element]]
  }, USE.NAMES = FALSE)))

# set values of 'sub_cell_type1' column
colData(normalizedSExpr)$sub_cell_type1 <- paste(
  colData(normalizedSExpr)$general_cell_type, 
  colData(normalizedSExpr)$parental_cell_type,
  sep = '-')

# Set feature_id for of 'rowData' of the input
rowdata <- rowData(normalizedSExpr)
rowdata$feature_id <- rownames(rowdata)
rowData(normalizedSExpr) <- rowdata

# Set assay 'exprs' same as 'counts'
assays(normalizedSExpr)$exprs <- assay(normalizedSExpr, "counts")

coldata <- colData(normalizedSExpr)  
# Set 'donor_tissue' as 'parental_cell_type'
colData(normalizedSExpr)$donor_tissue <- coldata$parental_cell_type
# Set the values of 'experiment_id' as values in 'SRX_accession' column of
# the coldata of the input object
sampleMetaData <- read.csv(sampleMetadataPath, header = TRUE)
# Set the values in 'platform_id' as values in 'Platform' column of the
# coldata of the input object.
colData(normalizedSExpr)$platform_id <- colData(normalizedSExpr)$Platform
# Set the values in 'sample_id' as SRR accessions, which are the rownames
# of the coldata of the input object.
colData(normalizedSExpr)$sample_id <- rownames(colData(normalizedSExpr))

colData(normalizedSExpr)$experiment_id <- unname(unlist(apply(colData(normalizedSExpr), 1,
    function(row) {
      formatedType <- paste(row[['general_cell_type']], 
                            row[['parental_cell_type']], 
                            sep = '-')
      transitionSel <- which(cell.change$test == formatedType)
      if (length(transitionSel) == 0) {
        return('standard')
      }
      cell.change[transitionSel, ]['target']
    })))

pcaPlot(normalizedSExpr)
## Warning in if (plot == "2d") {: the condition has length > 1 and only the first
## element will be used
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
print("Content of cell.change")
## [1] "Content of cell.change"
print(cell.change)
##    start        test target
## 1    ESC    dCDM-ESC    CDM
## 2    ESC    HPTL-ESC    HPT
## 3    ESC  dDPNEU-ESC  DPNEU
## 4    IPS    dNEU-IPS    NEU
## 5    ESC    dNEU-ESC    NEU
## 6    ESC  dNEUPR-ESC  NEUPR
## 7    ESC    dRPE-ESC    RPE
## 8    IPS    dRPE-IPS    RPE
## 9    IPS    dCDM-IPS    CDM
## 10   IPS  dNEUST-IPS  NEUST
## 11   IPS    dPCB-IPS    PCB
## 12   ESC    dEDD-ESC    EDD
## 13   ESC   dFEDD-ESC   FEDD
## 14   ESC dHPTEDD-ESC HPTEDD
## 15   ESC   dEHPT-ESC    HPT
## 16   ESC   dLHPT-ESC    HPT
## 17   IPS    dEDD-IPS    EDD
## 18   IPS   dFEDD-IPS   FEDD
## 19   IPS dHPTEDD-IPS HPTEDD
## 20   IPS   dEHPT-IPS    HPT
## 21   IPS   dLHPT-IPS    HPT
## 22   FIB    INEU-FIB    NEU
print("'normalizedSExpr' returns")
## [1] "'normalizedSExpr' returns"
print(normalizedSExpr)
## class: SummarizedExperiment 
## dim: 49916 445 
## metadata(0):
## assays(3): counts calls exprs
## rownames(49916): ENSG00000223972 ENSG00000227232 ... ENSG00000275987
##   ENSG00000277475
## rowData names(6): GeneSymbol mean ... merged feature_id
## colnames(445): SRR1783834 SRR1783835 ... ERR1247125 ERR1247126
## colData names(62): category cell_type ... sample_id experiment_id

Calculate ‘OnOff’ scores

group.OnOff <- OnOff(normalizedSExpr, cell.change, out.put="marker.list")
## Warning in NSBS(i, x, exact = exact, strict.upper.bound = !allow.append, :
## subscript is an array, passing it thru as.vector() first
print(summary(group.OnOff))
##         Length Class      Mode
## scores  10     data.frame list
## markers  8     data.frame list
individ.OnOff <- OnOff(normalizedSExpr, cell.change, out.put="individual")
## Warning in NSBS(i, x, exact = exact, strict.upper.bound = !allow.append, :
## subscript is an array, passing it thru as.vector() first
## To process 4 cell transitions
## Now processing transition ESC -> HPT
## 
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barplot.out <- BarplotOnOff(normalizedSExpr, group.OnOff$scores)
## Warning in NSBS(i, x, exact = exact, strict.upper.bound = !allow.append, :
## subscript is an array, passing it thru as.vector() first

barplot.out
## $GroupComparisonsForPlot
##    start target      test markers.start markers.target start.mkrs.in.test
## 15   ESC    HPT dEHPT-ESC           566            785                327
## 16   ESC    HPT dLHPT-ESC           566            785                309
## 20   IPS    HPT dEHPT-IPS           763            548                482
## 21   IPS    HPT dLHPT-IPS           763            548                515
## 2    ESC    HPT  HPTL-ESC           566            785                299
## 7    ESC    RPE  dRPE-ESC           445             50                 61
## 8    IPS    RPE  dRPE-IPS           717             20                484
##    target.mkrs.in.test loss.start.mkrs gain.target.mkrs OnOffScore
## 15                  83       0.4222615        0.1057325  0.5279940
## 16                  91       0.4540636        0.1159236  0.5699872
## 20                 118       0.3682831        0.2153285  0.5836116
## 21                 149       0.3250328        0.2718978  0.5969306
## 2                  105       0.4717314        0.1337580  0.6054894
## 7                    4       0.8629213        0.0800000  0.9429213
## 8                   19       0.3249651        0.9500000  1.2749651
## 
## $OnOffBarplotData
##    position   markers       markername      tags
## 1     Below 0.4222615  loss.start.mkrs dEHPT-ESC
## 2     Below 0.4540636  loss.start.mkrs dLHPT-ESC
## 3     Below 0.3682831  loss.start.mkrs dEHPT-IPS
## 4     Below 0.3250328  loss.start.mkrs dLHPT-IPS
## 5     Below 0.4717314  loss.start.mkrs  HPTL-ESC
## 6     Below 0.8629213  loss.start.mkrs  dRPE-ESC
## 7     Below 0.3249651  loss.start.mkrs  dRPE-IPS
## 8     Above 0.1057325 gain.target.mkrs dEHPT-ESC
## 9     Above 0.1159236 gain.target.mkrs dLHPT-ESC
## 10    Above 0.2153285 gain.target.mkrs dEHPT-IPS
## 11    Above 0.2718978 gain.target.mkrs dLHPT-IPS
## 12    Above 0.1337580 gain.target.mkrs  HPTL-ESC
## 13    Above 0.0800000 gain.target.mkrs  dRPE-ESC
## 14    Above 0.9500000 gain.target.mkrs  dRPE-IPS
tmp.time <- system.time(cs <- CosineSimScore(normalizedSExpr, cell.change,
                                             iqr.cutoff=0.1))
## Warning in NSBS(i, x, exact = exact, strict.upper.bound = !allow.append, :
## subscript is an array, passing it thru as.vector() first
PlotCosineSimHeatmap(cs$cosine.general.groups, "general groups",
                     width=20, height=20, x=-20, y=3)
## png 
##   2
cellscore <- CellScore(normalizedSExpr, cell.change, individ.OnOff$scores,
                       cs$cosine.samples)
## Warning in NSBS(i, x, exact = exact, strict.upper.bound = !allow.append, :
## subscript is an array, passing it thru as.vector() first

## Warning in NSBS(i, x, exact = exact, strict.upper.bound = !allow.append, :
## subscript is an array, passing it thru as.vector() first

## Warning in NSBS(i, x, exact = exact, strict.upper.bound = !allow.append, :
## subscript is an array, passing it thru as.vector() first
ScatterplotCellScoreComponents(cellscore, cell.change, FALSE)

pdf(file="CellScoreReport_PerTransition.pdf", width=7, height=11)
CellScoreReport(cellscore, cell.change, group.OnOff$markers, normalizedSExpr)
dev.off()
## png 
##   2